用超盒分割输入空间，提升抗体生产过程预测精度与速度
Hyperbox Mixture Regression for Process Performance Prediction in Antibody Production
- 通过动态生成超盒划分输入空间，提升模型学习效率
- 在106个生物反应器数据上，15天预测误差低于对比模型
- 适合需要高可解释性的工业生物过程预测场景
本文针对单克隆抗体（mAb）生产中传统统计方法难以应对时间序列复杂性和高维数据的问题，提出一种新型超盒混合回归（Hyperbox Mixture Regression, HMR）模型。该模型采用基于超盒的输入空间划分策略，在保证预测准确性的同时有效管理生物过程数据中的不确定性。HMR 在单次遍历中动态生成超盒，显著提升学习速度并降低计算复杂度。实验基于包含106个生物反应器的数据集，评估了模型在15天培养周期内对关键质量属性的预测表现。结果表明，HMR 在准确率和训练速度上均优于对比模型，并在不确定条件下保持良好的可解释性与鲁棒性。研究证实HMR在生物工艺预测分析中具有重要应用潜力。
原文摘要 · Abstract (English)
This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall short due to time-series data's complexity and high dimensionality. We propose a novel Hyperbox Mixture Regression (HMR) model which employs hyperbox-based input space partitioning to enhance predictive accuracy while managing uncertainty inherent in bioprocess data. The HMR model is designed to dynamically generate hyperboxes for input samples in a single-pass process, thereby improving learning speed and reducing computational complexity. Our experimental study utilizes a dataset that contains 106 bioreactors. This study evaluates the model's performance in predicting critical quality attributes in monoclonal antibody manufacturing over a 15-day cultivation period. The results demonstrate that the HMR model outperforms comparable approximators in accuracy and learning speed and maintains interpretability and robustness under uncertain conditions. These findings underscore the potential of HMR as a powerful tool for enhancing predictive analytics in bioprocessing applications.
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